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library(janitor)
Attaching package: 'janitor'
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chisq.test, fisher.test
library(dplyr)library(ggplot2)library(maps)
Attaching package: 'maps'
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Load in Data
data_wd <-"/Users/oliviaholt/Documents/eds240/Holt-eds240-HW4/data"# import data ----fourteener_data <-read_csv(file.path(data_wd, "14er.csv"))
Rows: 58 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): Mountain Peak, Mountain Range, fourteener, Standard Route, Difficu...
dbl (10): ID, Elevation_ft, Prominence_ft, Isolation_mi, Lat, Long, Distance...
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Clean Data
# clean data ----fourteener_clean <- fourteener_data %>%clean_names()
Which option do you plan to pursue: Option 1
Restate your question. Has this changed at all since HW #1:
What makes a route the most popular in high traffic? prominence, isolation, elevation etc.
Explain which variables from your data set(s) you will use to answer your question(s):
traffic (high), elevation gain, prominence, distance, latitude, longitude.
In HW #2, you should have created some exploratory data viz to better understand your data. You may already have some ideas of how you plan to formally visualize your data, but it’s incredibly helpful to look at visualizations by other creators for inspiration. Find at least two data visualizations that you could (potentially) borrow / adapt pieces from. Link to them or download and embed them into your .qmd file, and explain which elements you might borrow (e.g. the graphic form, legend design, layout, etc.).
The bubble plot I saw from data to viz and thought it would be a minimal but effective way to show my question. And the ternary plot from discussion I really liked so I wanted to try one of those.
Hand-draw your anticipated three visualizations (option 1) or infographic (option 2). Take a photo of your drawing and embed it in your rendered .qmd file – note that these are not exploratory visualizations, but rather your plan for your final visualizations that you will eventually polish and submit with HW #4.